A dynamical framework to relate perceptual variability with multisensory information processing

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Psychophysics

Background:

• Multisensory processing integrates information from various sensory streams (e.g., vision, audition) for perception.
• The McGurk effect demonstrates illusory speech sound perception from incongruent audiovisual stimuli.
• Auditory-visual lag and unisensory perturbations significantly modulate multisensory perception, yet quantitative models are scarce.

Purpose of the Study:

• To propose a dynamic systems model for multisensory processing.
• To investigate the role of coupling parameters and time delays in multisensory integration.
• To provide a quantitative framework for understanding perceptual variability in multisensory environments.

Main Methods:

• Developed a dynamic systems model with two unisensory and one multisensory subsystem (nodes).
• Incorporated biophysically inspired coupling parameters and time delays between nodes.
• Analyzed network synchronization patterns based on audiovisual (AV) lag.

Main Results:

• Zero AV lag maximized the synchronization of network nodes.
• Non-zero AV lags led to decreased synchronization.
• Attractor states of the model correlate with stable perceptual experiences.

Conclusions:

• The proposed dynamic model offers a quantitative framework for understanding multisensory information processing.
• Network synchronization, driven by AV lag, is crucial for stabilizing perception.
• The model elucidates the interplay between psychophysical variables and neural interactions in multisensory perception.